The proper identification of the difficulty levels of reading materials is a vital aspect of the language learning process. It enables teachers and educators alike to assign appropriate materials to young learners in which they can fully comprehend, preventing boredom and disinterest  [1]}. However, assessing readability presents challenges, particularly when you have a large corpus of text to sift through. Manually extracting and calculating a wide range of linguistic features can be time-consuming and expensive and can lead to subjectivity of labels due to human errors [2]}. To tackle this problem, more and more research in the field have focused on experimenting with automated methods for extracting possible linguistic predictors to train models for readability assessment.
